Much has been written about Data Scientist as the sexiest job of the 21st century, but this is only half of the story. Data science as a growing discipline and competitive advantage isn’t possible without a modernized, secure architecture in which to capture, store, organize, transport and protect the big data used by Data scientists and business executives. This behind-the-scenes database and cloud infrastructure is the responsibility of Big Data engineers (or Data engineers), and many companies have realized that these hires come first as they cultivate Data science maturity.
In the Internet of Things, immense streams of big data from endless sources are produced at an unprecedented speed every day. Due to the size and ubiquity of this "big" data, traditional methods of storing and processing that data have fallen short, now relying on more powerful databases and modern, secure cloud-based storage services like AWS and Azure. Optimizing this data for business and operational use requires specialized data engineering expertise to build, maintain, and manage the big data environment. This enabling tech ecosystem and its related governance, processes and practices are often referred to as part of DevOps (or MLOps) for machine learning applications.
The hiring and support of Big Data engineers is essential to building an organization's foundational big data and data science competencies. In this guide, we’ll explain how engineering and data science connects to big data, the roles and responsibilities of a Big Data engineer, and how you can prepare for a career in this exciting, growing field.
What is Big Data Engineering?
Big Data engineering is the practice of using ever-evolving tech solutions and platforms to manage the capture and ingestion, secure storage, transport, integration and use of big data within an organization. Building and maintaining massive data processing systems, powerful databases and cloud-based services in large-scale computing environments are part of Big Data engineering.
What is a Big Data Engineer Responsible For?
A Big Data engineer is primarily responsible for the end-to-end collection, organization and structure, and management of big data, using their knowledge, software development and programming skills and other tech competencies to develop enterprise solutions, whether software systems and APIs, databases, cloud services, tools and frameworks, or the integration of all these solutions.
Depending on an organization’s level of data science maturity, sometimes a (Big) Data Engineer’s role will extend into the analysis and visualization of big data, typically the domain and responsibility of Data scientists. This also goes the other way around, with Data scientists occasionally functioning as Big Data engineers.
A Lead Big Data Engineer will typically have additional responsibilities and skills, including the leadership and mentorship of other data engineers and/or data scientists, a higher level of business acumen (e.g., the economics of energy, HealthTech or FinTech), and the ability to contribute to (or lead) data acquisition strategy through partnerships or other means.
What Does a Big Data Engineer Do?
A Data engineer, in general, is responsible for developing the integrated big data systems and architecture that allow Data scientists to structure and transform big data into strategic insights and recommendations. The responsibilities of a Big Data engineer can vary but most likely include:
- Collaborate with other software engineers, data scientists, data architects, IT or DevOps teams, and business managers or executives to establish objectives, execute projects (often working in Agile or Scrum) and deliver against key outcomes
- Build and maintain data management systems and solutions to meet specific requirements, including security and scalability
- Develop computer programs to audit and structure big data at scale
- Seek new opportunities to acquire, clean and improve the use of big data, constantly seeking out new tech solutions or business ideas
What Are the Skills Required for Big Data Engineers?
Big Data engineers need a background in software engineering and programming. Important Big Data engineer skills include:
- Database Knowledge: The structure and language of databases are core skills for Data engineers. Data storage, organization, and querying are key aspects of a Big Data engineering job.
- Data Warehouse Knowledge: Big Data engineers must be skilled in structured query language (SQL) and NoSQL-based data warehousing structures and languages. Other important data warehouse knowledge includes object database, document store, native multi-model database, and key-value cache.
- Programming
- Cloud Knowledge: Cloud storage and processing is a preferred tool of Big Data engineers. It surpasses hard-drive servers in distributed access and scalability.
- Business Acumen: The end goal for most Big Data engineers is often to improve profits and efficient processes for an organization. An understanding of basic business principles is important for all aspects of Big Data engineering, from developing project goals to communicating with the executive team.
- Machine Learning: For sorting and processing large amounts of data in a short time, machine learning is essential. Machine learning algorithms learn by processing data sets, so machine learning and big data are inextricably linked.
- Statistics: This is a primary skill for Data scientists who work with Big Data engineers. Data engineers should understand the basics of statistics to communicate effectively with the Data scientists and lead the team.
Does Big Data Engineering Require Coding?
Yes, Python, Java and SQL are among the three most used programming languages for Big Data engineers. Many data engineers also program in Scala. R is generally preferred in the data science and analysis space for statistical modeling and machine learning algorithms but is used less frequently than Python for data engineering and full-stack engineering.
For people starting out with coding, Rice recommends beginning with the Python language. Check out our Fundamentals of Computing and Introduction to Python Scripting online specializations, both available on Coursera.
Big Data Engineer vs. Data Scientist
At a larger corporation or a data science "mature" organization, while they have an overlapping skill set and will collaborate on initiatives, a Big Data engineer and a Data scientist are distinctly different positions. A Big Data engineer develops and maintains the increasingly cloud-based architecture that captures, organizes and secures big data. A Data scientist analyzes that data at scale to answer big questions, make better recommendations, and predict future outcomes.
At a smaller company or company earlier on in its data science journey, the Data engineer and Data science roles can be more blended, with hires wearing more hats. For some, this is an exciting opportunity to gain practical, real-world experience and skills that will make them more well-rounded professionals. Other professionals may prefer greater structure, clarity and delineation of these roles.
Average Big Data Engineer & Data Scientist Salaries
Data Engineer and Data Scientist occupations tend to be among the highest-paying in the country because of their advanced technical skills and fast-growing labor market demand.
Here's how Big Data Engineer and Data Scientist salaries compare:
- Data Engineer: According to Indeed, Data Engineers typically make between $85,915-$220,862, with the average salary in the United States being approximately $137,751. Another salary source, Glassdoor, reports an average total salary for a U.S.-based Big Data engineer at about $116,106, ranging as high as $183,000. Total compensation may include bonuses, profit sharing and other incentives where applicable. Years of experience, level of education, and job location can all affect salary ranges.
- Full Stack Software Engineers: According to Glassdoor, Full Stack Software Engineers can make between $77,000-$191,000 with the average salary in the United States being $99,023. Additional pay is estimated to be $21,297 which can include bonuses, tips, commission and profit sharing.
- Data Scientist: According to Salary.com, the average Data Scientist salary in the United States is $138,365. Salaries can also range from $123,276-$152,489 depending on education, certifications and years of job experience.
How to Become a Big Data Engineer
Data Engineers are in-demand professionals responsible for the advanced data pipelines and infrastructure within their organizations. Learn about the undergraduate studies, work experience and common continuing education for those aspiring to pursue this career path.
Education
Data engineers typically have a background in computer science, engineering, applied math, or other related IT fields. Because the role requires heavy technical knowledge, most data engineering jobs require at least a relevant bachelor’s degree in a related discipline and, often, a graduate degree in computer science.
Work Experience
Most Data Engineer positions require at least 2-5 years work experience with SQL, schema design, dimensional modeling, and/or software development experience in Big Data technologies like Spark, Hive, Hadoop or Apache Kafka. It's also important for data engineers to demonstrate strong Python scripting and/or Java development skills.
Continued Education
Working professionals often pursue one of the following data engineering certifications or specializations:
- Google IT Support Professional Certificate
- Google Cloud's Data Engineering, Big Data and Machine Learning on GCP Specialization
- Microsoft Certified: Azure Fundamentals Certification
- Cloudera Data Platform Generalist
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Oracle's PL/SQL Developer Certified Associate
Preparing for a Career as a Big Data Engineer
Preparation for a Big Data engineering position begins with a solid foundation in computer science concepts and programming skills. It's wise to gain at least 1-2 years work experience in IT or databases where you can practice and expand your core technical and problem-solving skills. The MCS@Rice degree program curriculum was designed with data engineering career pathways in mind, so you may also consider a master’s degree in computer science to help you further expand your skills and advance your career as a Big Data engineer.